Cody Hyndman, PhD

Contact information

Email:

cody.hyndman@concordia.ca

Website:

Cody Hyndman

Biography

Education

Ph.D.:  University of Waterloo, Canada 2005

Positions

06/2023-present:  (Full) Professor
07/2017-06/2023: Department Chair (Academic Unit Head)
06/2011-05/2023: Associate Professor
07/2006-05/2011: Assistant Professor

Awards

06/2023: Concordia Academic Leadership Award

Teaching activities

Recent Courses

MACF 401: Mathematical and Computational Finance I
MACF 402: Mathematical and Computational Finance II

Research activities

Recent Publications

Kratsios, A, Hyndman, C. Generative Ornstein Uhlenbeck Markets via Geometric Deep Learning. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14072. Springer, Cham. 2023; Part II: 605-614.

Kratsios A, Hyndman C. NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation. Journal of Machine Learning Research. 2021; 22(92): 1-51.

Kratsios A, Hyndman C. Deep Arbitrage-free Learning in a Generalized HJM Framework via Arbitrage-Regularization. Risks. 2020; 8(2), 40: 1-30.

Wang R, Hyndman C, Kratsios A. The Entropic Measure Transform. The Canadian Journal of Statistics. 2020; 48(1): 97-129.

Hillairet C, Hyndman C, Jiao Y, Wang, R. Trading against disorderly liquidation of a large position under asymmetric information and market impact. ESAIM: Proceedings and Surveys. 2017; 56: 42-71.

Hyndman C, Oyono Ngou P. A Convolution Method for Numerical Solution of Backward Stochastic Differential Equations. Methodology and Computing in Applied Probability. 2017; 19(1): 1-29.

Publications

A full list of my publications and preprints is available on this webpage:
http://mypage.concordia.ca/alcor/chyndman/research.html

Funding

Research and Funding Opportunities for Students

Co-founder of the NSERC CREATE Program on Machine Learning in Quantitative Finance and Business Analytics (FIN-ML).

Students (graduate and undergraduate) pursuing a degree in the Department of Mathematics and Statistics under my supervision may apply for FIN-ML scholarships and participate in the training program, research, and industrial internships.

Research Grants (Current)

NSERC Discovery
MITACS Accelerate
NSERC Create